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Journal : journal of engineering and science application

Application of the K-Means Algorithm for the Grouping of Regional Income Patterns in Kudus Regency Alif Miftachul Nasikhah; Ade Ima Afifa Himayati; Findasari Findasari
Journal of Engineering and Science Application Vol. 3 No. 2 (2026): Mei-Oktober
Publisher : Institute Of Advanced Knowledge and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.69693/jesa.v3i2.39

Abstract

Regional revenue is one of the indicators of regional financial ability that needs to be analyzed to determine the realization of revenue. Analyses that are still descriptive have not been able to group data based on similar characteristics. This study aims to apply the K-Means algorithm to classify the regional income pattern of Kudus Regency based on monthly income realization data. The research uses a quantitative approach with secondary data in the form of the realization of regional revenue in Kudus Regency in 2020–2024 obtained from the Regional Revenue, Finance, and Asset Management Agency (BPPKAD) of Kudus Regency. Data processing is carried out using the RapidMiner application through the Read Excel, Set Role, Normalize, K-Means Clustering, and Performance stages, The number of clusters is set to three (k = 3). The results of the study showed that the K-Means algorithm succeeded in grouping data into three Cluster 0 clusters consisting of 4 data, namely September, October, November, and December. Cluster 1 consists of 1 data, namely August, while Cluster 2 consists of 7 data, namely January, February, March, April, May, June, and July.  Centroid analysis showed that each cluster had different characteristics, while evaluation using Performance Vector yielded a Davies-Bouldin Index value of -0.444 which showed good grouping results based on the RapidMiner evaluation. The results of this study are expected to be supporting information in the evaluation and planning of regional revenue management in Kudus Regency
Modeling Outpatient Visits Using Poisson and Negative Binomial Regression at Sarkies Aisyiyah Kudus Hospital Nur Hakimah; Ade Ima Afifa Himayati; Findasari Findasari
Journal of Engineering and Science Application Vol. 3 No. 2 (2026): Mei-Oktober
Publisher : Institute Of Advanced Knowledge and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.69693/jesa.v3i2.50

Abstract

Outpatient visits are an important indicator of healthcare service utilization and play a significant role in hospital service planning and resource management. This study aims to model the number of outpatient visits at Sarkies Aisyiyah Kudus Hospital using Poisson and Negative Binomial regression and to identify the factors influencing outpatient visits. This study used secondary data consisting of 41 daily observations from November 1 to December 12, 2025. The response variable was the daily number of outpatient visits, while the predictor variables were the number of BPJS patients, the number of operating clinics, and the number of doctors on duty. Data analysis was performed using RStudio. The results showed that all three predictor variables had a positive relationship with outpatient visits in the Poisson regression model. However, the overdispersion test produced a dispersion value of 10.04, indicating that the Poisson assumption of equidispersion was not satisfied. The Negative Binomial regression model showed that the number of BPJS patients and the number of clinics had a positive and significant effect on outpatient visits, while the number of doctors had a positive but non-significant effect. The Negative Binomial model provided a better fit, with an AIC of 435.58, compared with 673.48 for the Poisson model. Therefore, the Negative Binomial regression model was selected as the more appropriate model for outpatient visit data at Sarkies Aisyiyah Kudus Hospital.